Performance Analysis on Health and Safety Issues of Companies from the Slaughterhouse Industry - Ismael Cristofer Baierle; Miguel Afonso Sellitto; Jones Luis Schaefer; Jaqueline de Moraes; Jairo Koncimal; Elpidio Oscar Benitez Nara (International Journal of Supply Chain Management, 2019)
The objective of this article is to analyses the performance of companies of the slaughterhouse industry in health and safety issues. The research method is the quantitative modelling. The main research technique uses a mixed method based on multi-attribute utility method (MAUT) and artificial neural networks (ANN). The research object are 34 slaughterhouse companies located in Southern Brazil. Then, we ranked the companies and modeled their decision trees using the MAUT method. From these results, neural networks were used to benchmark and compare the methods. This resulted in a linear equation that represents the closest solution to the ideal and percentage error in the decision trees resolution. Thus, neural networks are most efficient, because they indicate which KPIs (key performance indicators) most influence the organizations performance. We numerically present the gain of information and the margin of error, concluding that some KPIs do not influence competitiveness without requiring controls. The academic and social contribution is that through the union of MAUT and neural networks we can measure the performance and select the main KPIs that need to be controlled for any type of industry.